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About this paper
Paras Dahal, Anton Bakhtin, Taco Cohen, Zhengxing Chen, Carole-Jean Wu, Rob Fergus, Scott Yih, Gabriel Synnaeve and 4 more
The paper presents a meta-reasoning controller for agents reconstructing software from documentation and an execute-only reference program. The controller assesses progress, proposes and evaluates computations against the remaining model-call budget, then dispatches selected artifacts or stops. On 200-task ProgramBench, GPT-5.5 reaches 71.5% versus 63.7% for direct control at 1,200 calls, but gains do not hold at every budget.
Key insights
- Separating meta-level control from task execution helps agents allocate additional computation more productively, although controller overhead can hurt performance when budgets are small.
- The system improves coverage of correct candidates and often judges candidate quality more accurately than workers’ self-confidence; however, recognizing and selecting a correct candidate remains an independent challenge.
- Compact controller notes greatly reduce context growth compared with retaining the full interaction history, but lossy summaries and repeated reconsideration can introduce new failure modes.
Summary by alphaXiv.
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